Safe Passage Program

Introduction

This project estimates the impact of the Safe Passage Program in the City of Chicago, which starts at Year 2015. The Chicago Public Schools Safe Passage program is offered to select schools to provide a positive, trusted adult presence for students as they travel to and from school. You may find more information about this program Here. The data of this project is from the website of Chicago Public Schools.

 

Section 1. Loading and Cleaning Data

First, we load the dataset of the school attendance rate (excluded data of Year 2021 due to Covid).

 

School ID School Name Network Group Grade time attendence
NA CITYWIDE NA All (Excludes Pre-K) NA 2013 92.5
400008 ACT CHTR HS NA All (Excludes Pre-K) NA 2013 NA
400009 GLOBAL CITIZENSHIP Charter All (Excludes Pre-K) NA 2013 96.5
400010 ACE TECH HS NA All (Excludes Pre-K) NA 2013 90.8
400011 LOCKE A Charter All (Excludes Pre-K) NA 2013 94.4
400012 AMANDLA HS NA All (Excludes Pre-K) NA 2013 93.4

 

Section 2. School Distribution

After joining the dataset of school attendance rate and program participation, we can map the location of all schools with thier participation in the Safe Pass Passage Program. Through the map, we can observe that Treated schools and Control schools are equally distributed across the city. Several Treated schools are concentrated in the South part of Chicago.

 

 

Section 3. Crime Distribution

Let’s first look at the distribution of Crimes that took place within a 50 yards distance from schools. It seems that crimes spreads out of the whole city, with a small concentration in the center of Chicago.

 

 

How about the distribution of Crimes with different distance to schools? By looking at the distribution of Crimes with colors (Red indicating very dangerous, Green indicating relatively safe), we can observe that there’s no apparent safe area —— all areas are scattered with crimes that are very close to schools.
 

 

What type of Crimes happen the most frequent? By plotting the top 10 frequent Crime types, the most frequent type of Crime is Theft, followed by Battery, Criminal Damage and Narcotics.

 

 

Section 5. Regression Analysis

Finally, using Fixed effects Regression, we can estimates the impact of the Safe Passage Policy on both crime occurrence close to schools and school attendance.

 

## 
## t test of coefficients:
## 
##             Estimate Std. Error t value  Pr(>|t|)    
## InPolicyYes -18.6919     4.3766 -4.2709 2.017e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 

Running fixed effects regression of total crime in the presence of the Safe Passage Policy, the coefficient of joining the Safe Passage Program is -18.6919 (statistically significant). This means that if a school joins the Safe Passage Program, the Average Crimes per year will decrease by 18.6919.

 

## 
## t test of coefficients:
## 
##             Estimate Std. Error t value  Pr(>|t|)    
## InPolicyYes  2.24453    0.49242  4.5582 5.399e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 

Running fixed effects regression of total crime in the School Attendance Rate, the coefficient of attendance rate is 2.24453 (statistically significant). This means that if a school joins the Safe Passage Policy Program, the The school attendance rate will increase 2.25%.

 

Conclusion

To conclude, this project used fixed effects regression to estimates the impact of the Safe Passage Program on both crime occurrence close to schools and school attendance. The result indicates that the Safe Passage Policy helps to reduce the crime occurrence close to schools and increases the school attendance rate. The Government and Education Department could consider expand this safety program to benefit more schools. Overall, a safe environment for children is the prerequisite of their future as well as the world’s future.
 

 

 

2022 Data and Policy Summer Scholar Program